• Transport accounts for ~10% of global CO2 emissions.
• Innovation in green automotive technologies is crucial for sustainability.
• EU has implemented policies to support EV adoption.
• However EU and OECD countries have stepped back from competing with China in the green automotive sector.
• Key drivers of innovation: Demand, Technology, and Regulation.

Ursula von der Leyen: “Europe must lead the green transition, and mobility is at the core of this transformation.”

• Rapid Growth: The automobile industry expanded quickly in the 1890s.
• Competing Technologies: Electric, steam, and gasoline cars all vied for market share.
• Market Trends: Although U.S. developments lagged Europe, steam cars (e.g., the Locomobile) were initially the most popular.
  

  Locomobile Steam Roundabout 1900

• Strong 1899 Sales: In 1899, 1,575 electric vehicles were sold alongside 1,681 steam and 936 gasoline cars.
• Strategic Moves: The Electric Vehicle Company ordered 200 vehicles and announced plans for large-scale electric taxicabs.
• Technical Advantages: Electric vehicles shared components with streetcars and showcased advanced performance (e.g., “La Jamais Contente” reached 100 km/h).

• Strong 1899 Sales: In 1899, 1,575 electric vehicles were sold alongside 1,681 steam and 936 gasoline cars.
• Strategic Moves: The Electric Vehicle Company ordered 200 vehicles and announced plans for large-scale electric taxicabs.
• Technical Advantages: Electric vehicles shared components with streetcars and showcased advanced performance (e.g., “La Jamais Contente” reached 100 km/h).

Jamais Contente 1899

• Rising Competition: Despite a doubling of electric vehicle sales from 1899 to 1909, gasoline car sales increased more than 120 times.
• Market Shift: The rapid expansion of gasoline technology, bolstered by factors like patent control and economies of scale, ultimately sidelined electric vehicles.
• However: Electric vehicle technologies never really disappeared, but survived in the market itself, in adjacent markets, and in different markets (e.g. battery for renewable and mobiles)
• Key Research Question: How do we account for the present renewed competition between different standards, both theoretically and empirically?

• Definition: Eco-innovations include new or modified products, processes, practices, systems, and institutional arrangements that benefit the environment and sustainability (Díaz-García et al., 2015; Rennings, 2000; Oltra et al., 2008a).
• Sector Significance: The automotive industry is a major contributor to greenhouse gas emissions and urban air pollution (Nunes & Bennett, 2008).
• Global Targets: Achieving net zero by 2050 (IEA, 2021; Bouckaert et al., 2021) requires phasing out new internal combustion engine (ICE) passenger cars by 2035.

• Environmental Awareness: The 1970s oil shocks and increasing environmental awareness spurred efforts to “green” the engine (Meadows et al., 1972; Faria & Andersen, 2017).
• Innovation Debates: Although electric vehicles (EVs) lower local emissions, they also pose challenges (e.g., battery-related issues, cost, autonomy) (ADL Report, 2016; Cecere et a. 2018).
• Industry Focus: Research highlights competing technological trajectories in the automotive sector, emphasizing product design and eco-innovation (Segarra-Ona et al., 2011; Sierzchula et al., 2012; Christensen, 2011; Mazzei et al., 2023).

• Lock-In Concept: “Carbon lock-in” occurs when investments in ICE technologies create inertia against greener alternatives (Unruh, 2000; Unruh, 2002).
• Three Lock-In Sources: Demand, supply, and regulatory lock-ins reinforce the dominance of ICE technology (Dijk & Yarime, 2010; Bjørnavold & Van Passel, 2017).
• Industry Example: The automotive cooling systems in the EU show how even well-intended regulations can inadvertently entrench existing technologies (Bjørnavold & Van Passel, 2017).

• Regulatory Drivers: Stringent and targeted environmental policies (e.g., fuel efficiency programs, emission standards) encourage eco-innovation (Kuik, 2007; Maldonado-Guzmán et al., 2024; Haščić et al., 2008).
• Technology Push: Collaborative R&D and supplier partnerships foster the development of new automotive technologies, including electric, hybrid, and fuel cell innovations (Potter & Graham, 2019; Wu et al., 2020; Scarpellini et al., 2012).
• Demand Pull: Market demand and consumer preferences for sustainable products drive eco-innovative efforts (Fernando et al., 2021; Peiró-Signes & Segarra-Ona, 2018).

• Two Trajectories:
  • Greenish Innovations: Incremental improvements aimed at enhancing ICE efficiency.
  • Green Innovations: Radical eco-innovations such as hybrid, electric, and fuel cell technologies (Aghion et al., 2016; Mazzei et al., 2023).
• Technological Competition: The “sailing ship effect” and “red queen effect” illustrate how incumbent technologies may persist even amid radical innovations (Sick et al., 2016; Barnett & Hansen, 1996; Derfus et al., 2008).
• Key Drivers: Shifts in global market demand, changes in fueling infrastructure, and climate policies (Dijk et al., 2013) are critical in determining which trajectory prevails.

• Mixed Outcomes: Eco-innovation drivers may either disrupt the dominant ICE paradigm or reinforce it by “greenishing” existing technologies (Dijk et al., 2013; Bergek et al., 2014)
• Policy Implications: An integrated approach combining regulation, R&D investments, and market incentives is essential to overcome path dependency (Costantini et al., 2015; Demirel & Kesidou, 2019; Cabigiosu & Lanzini, 2023).
• Future Gaps: Further empirical research is needed to assess the interplay of public policy, technological breakthroughs, and market demand in driving the diffusion of low-emission vehicles (Mazzei et al., 2023; Oltra & Saint Jean, 2009).
• In this paper: We test the correlation of technological factor, demand effect and regulation upon the two current greenish and green trajectories.

• Panel data from OECD countries (2005-2021), supplemented with OICA (2022) and IEA (2022)
• Key variables:
  • Dependent: Green & Greenish patent counts.
  • Independent: EPS, R&D investments, EV demand, GDPpc, Technological spezialization
  • Controls: Fuel prices, material footprint.

• Missing values accounted for 5.7% of the dataset.
• Multiple Imputation by Chained Equations (MICE) used.
• Verified Missing At Random (MAR) assumption.

• Two-way fixed effects model with Poisson estimation.
• It has been preferred over alternatives such as OLS-panel with log patent.
• 2 years lagged variables.
• Pseudo-demeaning.
• Accounting for non-linear effects of EPS, R&D, and GDP.
• Accounting for time and country fixed effects.

• Tested model with different lag structures.
• Splitting sample into high vs. low patent-producing countries.
• Bayesian Generalized Additive Models (GAMs) confirm results.

• EPS pushes GREEN only at high level, otherwise the opposite.
• There are spillovers between different paradigms
• EV demand pulls GREEN technologies and crowd-out GREENISH efforts
• Excess of specialization strenghen the ICE lock-in, but some is good.

$ echo 
marco.guerzoni@unimib.it

$ echo 
Feel free to contact me for research collaborations or discussions.

$ ping -c 1 127.0.0.1
PING 127.0.0.1 (127.0.0.1): 56 data bytes
64 bytes from 127.0.0.1: icmp_seq=0 ttl=64 time=0.045 ms

$ echo "Bicocca is home."

Retrieving Unit Fixed Effects

For each unit i: \[ \hat{\alpha}_i = \ln\left(\frac{\sum_{t=1}^T y_{it}}{\sum_{t=1}^T \exp(x_{it}\hat{\beta} + \hat{\gamma}_t)}\right) \]

Retrieving Time Fixed Effects

For each period t: \[ \hat{\gamma}_t = \ln\left(\frac{\sum_{i=1}^N y_{it}}{\sum_{i=1}^N \exp(x_{it}\hat{\beta} + \hat{\alpha}_i)}\right) \]

Estimation with fixest

# Two-way FE Poisson
mod_twfe <- feglm(y ~ x | id + time, 
                  family = "poisson",
                  data = panel_data)

# Retrieve FE
alphas <- fixef(mod_twfe)$id    # Unit FE
gammas <- fixef(mod_twfe)$time  # Time FE

Standard Errors

• For recovered fixed effects: \[ SE(\hat{\alpha}_i) = \sqrt{\frac{1}{\sum_{t=1}^T \exp(x_{it}\hat{\beta} + \hat{\gamma}_t)}} \]

# Compute SE
se_fe <- se(mod_twfe, 
            vcov = "cluster",
            cluster = ~ id)